This proposal is submitted in response to the new NIDDK announcement (PA-14- 058) calling for development of methodologies or biomarkers to help understand the pathophysiology of T1D type 1 diabetes (T1D) complications. An estimated 15-25% of the 25.8 million diabetic patients in this country will develop diabetic foot ulcers (DFU) at some point in their lives. DFU is the leading cause of non-traumatic lower-extremity amputations caused by non-healing wounds. Despite thousands of dressing products developed to treat chronic wounds, none has proven effective for DFU. As stated in the RFA, a significant obstacle for progress in developing drugs for diabetic complications is the paucity of biomarkers and surrogate endpoints for measuring the initiation, progression, and response to treatments of diabetic complications. Although many factors are involved in non-healing DFU, tissue ischemia is known to be a major factor contributing to poor wound healing. Ischemia may not be the initiating factor for DFU, because most ulcers start from a combination of neuropathy, pressure loading, and/or trauma. However, tissue ischemia is the main cause that hinders healing. Providing oxygen to chronic ulcers has not obtained stable results. This is because oxygen is only one of the ingredients required for HEP production. The specific aim of this phase I proposal is to explore the possibility of using high- energy phosphate (HEP) contents as a biomarker to predict the progression and recurrence of diabetic ulcers. We plan to use a diabetic animal wound model, with and without ischemia, to test the changes of HEP in these wounds, along with the expression of several key factors known to be critical to wound healing, such as vascular endothelial growth factors, cytokines, macrophages, collagens, and angiogenesis, and correlated them to obtain a preliminary picture of the relationship between these factors and the haling of diabetic wounds. The result may provide a preliminary indication whether the change of HEP content alone, or along with other key factors, will be better used to predict the likelihood of healing. This is a difficult job, but we already have a proof-of-concept.